GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules
學年 112
學期 2
出版（發表）日期 2024-07-14
作品名稱 GNN-RM: A trajectory completion algorithm based on graph neural networks and regeneration modules
作品名稱（其他語言）
著者 Hui, Lin
單位
出版者
著錄名稱、卷期、頁數 International Journal of Cognitive Computing in Engineering 5,　p.297-306
摘要 Data about vehicle trajectories assumes a crucial role in applications such as intelligent connected vehicles. However, missing values resulting from sensors and other factors frequently affect real trajectory data. Currently, it is challenging to utilize trajectory completion methods to generate accurate real-time results at an affordable computing cost. This paper proposes GNN-RM, a trajectory completion algorithm based on graph neural networks and regeneration modules, encompassing feature extraction, subgraph construction, spatial interaction graph, and trajectory regeneration modules. The feature extraction algorithm extracts influential data as feature vectors based on certain conditions and organizes these feature vectors into different subgraphs according to categories. The spatial interaction graph constructed through graph neural networks extracts spatial interaction features between vehicles and the environment, while the regeneration modules constructed by multi-head attention mechanisms extract temporal features of vehicles, thereby completing the missing trajectories. The experimental results demonstrate that GNN-RM can achieve higher trajectory completion accuracy with fewer input parameters than multiple baseline models.
關鍵字 Intelligent connected vehicles;Trajectory completion, Graph neural networks;Multi-head attention mechanism
語言 en
ISSN 2666-3074
期刊性質 國外
收錄於 EI Scopus
產學合作
通訊作者
審稿制度 是
國別 NLD
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126218 )